=Paper=
{{Paper
|id=Vol-1341/paper3
|storemode=property
|title=Applying Argument Extraction to Improve Legal Information Retrieval
|pdfUrl=https://ceur-ws.org/Vol-1341/paper3.pdf
|volume=Vol-1341
|dblpUrl=https://dblp.org/rec/conf/argnlp/Ashley14
}}
==Applying Argument Extraction to Improve Legal Information Retrieval==
https://ceur-ws.org/Vol-1341/paper3.pdf
Applying Argument Extraction to Improve Legal Information Retrieval
Kevin D. Ashley
University of Pittsburgh School of Law
Pittsburgh, Pennsylvania, USA 15260
ashley@pitt.edu
Abstract legal practice. A primary reason for this is the
well-known bottleneck in representing knowledge
Argument extraction techniques can likely from the legal texts (e.g., statutes, regulations, and
improve legal information retrieval. Any cases) that play such an important role in legal
effort to achieve that goal should take practice in a form so that the the computational
into account key features of legal reason- implementations can reason with them.
ing such as the importance of legal rules Meanwhile, legal information retrieval systems
and concepts, support and attack relations have proven to be highly functional. They pro-
among claims, and citation of authoritative vide legal practitioners with convenient access
sources. Annotation types reflecting these to millions of legal texts without relying on ar-
key features will help identify the roles of gument models or schemes, relying instead on
textual elements in retrieved legal cases in Bayesian statistical inference based on term fre-
order to better inform assessments of rele- quency. Users of legal information systems can
vance for users’ queries. As a result, legal submit queries in the form of a natural language
argument models and argument schemes description of a desired fact pattern and retrieve
will likely play a central part in the text numerous relevant cases.
annotation type system.
Useful as they are, however, legal information
retrieval systems do not provide all of the func-
1 Introduction
tionality that practitioners could employ. What
With improved prospects for automatically ex- IR system users often want “is not merely IR,
tracting arguments from text, we are investigat- but AR”, that is, “argument retrieval: not merely
ing whether and how argument extraction can im- sentences with highlighted terms, but arguments
prove legal information retrieval (IR). An immedi- and argument-related information. For example,
ate question in that regard is the role that argument users want to know what legal or factual issues the
models and argument schemes will play in achiev- court decided, what evidence it considered rele-
ing this goal. vant, what outcomes it reached, and what reasons
For some time, researchers in Artificial Intelli- it gave.” (Ashley and Walker, 2013a).
gence and Law have developed argument models, Recently, IBM announced its Debater project,
formal and dialectical process models to describe an argument construction engine which, given a
arguments and their relations. They have also corpus of unstructured text like Wikipedia, can au-
implemented these models in computer programs tomatically construct a set of relevant pro/con ar-
that construct legal arguments. Some of these guments phrased in natural language. Built upon
models employ argument schemes to provide se- the foundation of IBM’s Jeopardy-game-winning
mantics and describe reasonable arguments. Each Watson question answering system, the advent of
scheme corresponds to a typical domain-specific Debater raises some interesting related questions.
inference sanctioned by the argument, a kind of A central hypothesis of the Watson project was
prima facie reason for believing the argument’s to answer questions based on shallow syntactic
conclusion. See (Prakken, 2005, p. 234). knowledge and its implied semantics. This was
By and large, however, these argument models preferred to formally represented deep semantic
and schemes and their computational implementa- knowledge, the acquisition of which is difficult
tions have not had much of a practical effect on and expensive (Fan et al., 2012). If Debater is
applied to legal domains (See, e.g.,(Beck, 2014)), Factors, stereotypical fact patterns that
one wonders to what extent the same will be true strengthen or weaken a side’s argument in a legal
of Debater. In particular, to what extent will ex- claim, have been identified in text automatically.
plicit argumentation models and their schemes for Using a HYPO-style CBR program and an IR
the legal domain be necessary or useful for the ef- system relevance feedback module, the SPIRE
fort to extract legal arguments? And, can tech- program retrieved legal cases from a text corpus
niques in Debater be adapted to improve legal IR? and highlighted passages relevant to bankruptcy
law factors (Daniels and Rissland, 1997). The
2 Related Work SMILE+IBP program learned to classify case
summaries in terms of applicable trade secret
The seminal work on extracting arguments and law factors (Ashley and Brüninghaus, 2009),
argument-related information from legal case de- analyzed automatically classified squibs of new
cisions is (Mochales and Moens, 2011). Opera- cases, predicted outcomes, and explained the
tionally, the authors defined an argument as “a set predictions. (Wyner and Peters, 2010) presents a
of propositions, all of which are premises except, scheme for annotating 39 trade secret case texts
at most, one, which is a conclusion. Any argument with GATE in terms of finer grained components
follows an argumentation scheme. . . .” Using ma- (i.e., factoroids) of a selection of factors.
chine learning based on manually classified sen- Using an argument model to assist in represent-
tences from the Araucaria corpus, including court ing cases for conceptual legal information retrieval
reports, they achieved good performance on clas- was explored in (Dick and Hirst, 1991). More re-
sifying sentences as propositions in arguments or cently, other researchers have addressed automatic
not and classifying argumentative propositions as semantic processing of case decision texts for le-
premises or conclusions. Given a limited set of gal IR, achieving some success in automatically:
documents, their manually-constructed rule-based
argument grammar also generated argument tree • assigning rhetorical roles to case sentences
structures (Mochales and Moens, 2011). based on 200 manually annotated Indian de-
In identifying argumentative propositions, cisions (Saravanan and Ravindran, 2010),
Mochales and Moens achieved accuracies of 73%
and 80% on two corpora, employing domain- • categorizing legal cases by abstract West-
general features (including, e.g., each word, pairs law categories (e.g., bankruptcy, finance and
of words, pairs and triples of successive words, banking) (Thompson, 2001) or general top-
parts of speech including adverbs, verbs, modal ics (e.g., exceptional services pension, retire-
auxiliaries, punctuation, keywords indicating ment) (Gonçalves and Quaresma, 2005),
argumentation, parse tree depth and number of
subclauses, and certain text statistics.) For classi- • extracting treatment history (e.g., “affirmed”,
fying argumentative propositions as premises or “reversed in part”) (Jackson et al., 2003),
conclusions, their features included the sentence’s
length and position in the document, tense and • determining the role of a sentence in the legal
type of main verb, previous and successive case (e.g., as describing the applicable law or
sentences’ categories, a preprocessing classifi- the facts) (Hachey and Grover, 2006),
cation as argumentative or not, and the type of
rhetorical patterns occurring in the sentence and • extracting offenses raised and legal principles
surrounding sentences (i.e., Support, Against, applied from criminal cases to generate sum-
Conclusion, Other or None). Additional features, maries (Uyttendaele et al., 1998),
more particular to the legal domain included
whether the sentence referred to or defined a legal • extracting case holdings (McCarty, 2007),
article, the presence of certain argumentative and
patterns (e.g. “see”, “mutatis mutandis”, “having
reached this conclusion”, “by a majority”) and • extracting argument schemes from the Arau-
whether the agent of the sentence is the plaintiff, caria corpus such as argument from example
the defendant, the court or other (Mochales and and argument from cause to effect (Feng and
Moens, 2011). Hirst, 2011).
We aim to develop and evaluate an integrated (7) “constructed a demo speech with top claim
approach using both semantic and pragmatic (con- predictions”, and
textual) information to retrieve arguments from le- (8) was then “ready to deliver!”
gal texts in order to improve legal information re- Figure 1 shows an argument diagram con-
trieval. We are working with an underlying ar- structed manually from the video recording of De-
gumentation model and its schemes, the Default bater’s oral output for the example topic.
Logic Framework (DLF), and a corpus of U.S.
Federal Claims Court cases (Walker et al., 2011; 3 Key Elements of Legal Argument
Walker et al., 2014; Ashley and Walker, 2013a).
Like (Mochales and Moens, 2011) and (Sergeant, Debater’s argument regarding banning violent
2013), we plan to: video games is meaningful but compare it to the
legal argument concerning a similar topic in Fig-
1. Train an annotator to automatically identify ure 2. The Court in Video Software Dealers As-
propositions in unseen legal case texts, soc. v. Schwarzenegger, 556 F. 3d 950 (9th
Cir. 2009), addressed the issue of whether Cali-
2. Distinguish argumentative from non- fornia (CA ) Civil Code sections 1746-1746.5 (the
argumentative propositions and classify them “Act”), which restrict sale or rental of “violent
as premises or conclusions, video games” to minors, were unconstitutional un-
der the 1st and 14th Amendments of the U.S. Con-
3. Employ rule-based or machine learning mod-
stitution. The Court held the Act unconstitutional.
els to construct argument trees from unseen
As a presumptively invalid content-based restric-
cases based on a manually annotated training
tion on speech, the Act is subject to strict scrutiny
corpus, but also to
and the State has not demonstrated a compelling
4. Use argument trees to improve legal informa- interest.
tion retrieval reflecting the uses of proposi- In particular, the Court held that CA had not
tions in arguments. demonstrated a compelling government interest
that “the sale of violent video games to minors
Before sketching our approach for the legal should be banned.” Figure 2 shows excerpts from
domain, however, we note that IBM appears to the portion of the opinion in which the Court jus-
have developed more domain independent tech- tifies this conclusion. The nodes contain propo-
niques for identifying propositions in documents sitions from that portion and the arcs reflect the
and classifying them as premises in its Debater explicit or implied relations among those proposi-
system.1 tions based on a fair reading of the text.
On any topic, the Debater’s task is to “detect The callout boxes in Figure 2 highlight some
relevant claims” and return its “top predictions for key features of legal argument illustrated in the
pro claims and con claims.” On inputting the topic, Court’s argument:
“The sale of violent videogames to minors should
be banned,” for example, Debater: 1. Legal rules and concepts govern a court’s de-
(1) scanned 4 million Wikipedia articles, cision of an issue.
(2) returned the 10 most relevant articles,
(3) scanned the 3000 sentences in those 10 arti- 2. Standards of proof govern a court’s assess-
cles, ment of evidence.
(4) detected those sentences that contained
3. Claims have support / attack relations.
“candidate claims”,
(5) “identified borders of candidate claims”, 4. Authorities are cited (e.g., cases, statutes).
(6) “assessed pro and con polarity of candidate
claims”, 5. Attribution information signals or affects
1
See, e.g., http://finance.yahoo.com/blogs/ judgments about belief in an argument (e.g.,
the-exchange/ibm-unveils-a-computer- “the State relies”).
than-can-argue-181228620.html. A demo ap-
pears at the 45 minute mark: http://io9.com/ibms-
watson-can-now-debate-its-opponents- 6. Candidate claims in a legal document have
1571837847. different plausibility.
The
sale
of
violent
videogames
to
minors
should
be
banned.
Pro:
Exposure
to
violent
Con:
On
the
other
hand,
I
would
like
to
videogames
results
in
increased
note
the
following
claims
that
oppose
physiological
arousal,
aggression-‐ the
topic.
Violence
in
videogames
is
related
thoughts
and
feelings,
as
not
causally
linked
with
aggressive
well
as
decreased
pro-‐social
tendencies.
behavior.
Pro:
In
addiAon
these
violent
games
or
Con:
In
addiAon,
most
children
who
play
lyrics
actually
cause
adolescents
to
violent
videogames
do
not
have
commit
acts
of
real
life
aggression.
problems
Pro:
Finally,
violent
video
games
can
Con:
Finally,
video
game
play
is
part
of
an
increase
children’s
aggression.
adolescent
boy’s
normal
social
seDng.
Figure 1: Argument Diagram of IBM Debater’s Output for Violent Video Games Topic (root node)
Although the argument diagrams in Figures 1 “Special Masters” concerning whether claimants’
and 2 address nearly the same topic and share sim- compensation claims comply with the require-
ilar propositions, the former obviously lacks these ments of a federal statute establishing the National
features that would be important in legal argument Vaccine Injury Compensation Program. Under the
(and, as argued later, important in using extracted Act, a claimant may obtain compensation if and
arguments to improve legal IR). Of course, on one only if the vaccine caused the injury.
level this is not surprising; the Debater argument In order to establish causation under the rule
is not and does not purport to be a legal argument. of Althen v. Secr. of Health and Human Ser-
On the other hand, given the possibility of ap- vices, 418 F.3d 1274 (Fed.Cir. 2005), the peti-
plying Debater to legal applications and argumen- tioner must establish by a preponderance of the
tation, it would seem essential that it be able to evidence that: (1) a “medical theory causally con-
extract such key information. In that case, the nects” the type of vaccine with the type of injury,
question is the extent to which explicit argument (2) there was a “logical sequence of cause and ef-
models and argument schemes of legal reasoning fect” between the particular vaccination and the
would be useful in order to assist with the extrac- particular injury, and (3) a “proximate temporal
tion of the concepts, relationships, and informa- relationship” existed between the vaccination and
tion enumerated above and illustrated in Figure 2. the injury. Walker’s corpus comprises all deci-
sions in a 2-year period applying the Althen test of
4 Default-Logic Framework causation-in-fact (35 decision texts, 15-40 pages
Vern Walker’s Default Logic Framework (DLF) per decision). In these cases, the Special Masters
is an argument model plus schemes for evidence- decide which evidence is relevant to which issues
based legal arguments concerning compliance of fact, evaluate the plausibility of evidence in the
with legal rules. At the Research Laboratory for legal record, organize evidence and draw reason-
Law, Logic and Technology (LLT Lab) at Hofs- able inferences, and make findings of fact.
tra University, researchers have applied the DLF to The DLF model of a single case “integrates nu-
model legal decisions by Court of Federal Claims merous units of reasoning” each “consisting of one
1.
rule
and
2.
standard
legal
concepts
of
proof
5.
a8ribu9on
info
3.
support
/
a8ack
rela9ons
6.
plausibility
Figure 2: Diagram Representing Realistic Legal Argument Involving Violent Video Games Topic
conclusion and one or more immediately support- terarguments, (4) citation to the statute, 42 USC
ing reasons (premises)” and employing four types 300aa-11(c)(1)(C)(ii)), and to the Althen and Shy-
of connectives (min (and), max (or), evidence fac- face case authorities, (5) some attribution informa-
tors, and rebut) (Walker et al., 2014). For example, tion that signals judgments about the Special Mas-
Figure 3 shows an argument diagram representing ter’s belief in an argument (e.g., “Dr. Kinsbourne
the excerpt of the the DLF model of the special and Dr. Kohrman agree”), and (6) four factors that
master’s finding in the case of Cusati v. Secretary increase plausibility of the claim of causation.
of Health and Human Services, No. 99-0492V
(Office of Special Masters, United States Court 5 Legal Argument and Legal IR
of Federal Claims, September 22, 2005) concern-
Legal decisions contain propositions and argu-
ing whether the first Althen condition for showing
ments how to “prove” them. Prior cases provide
causation-in-fact is satisfied.
examples of how to make particular arguments in
The main point is that the DLF model of a le- support of similar hypotheses and of kinds of ar-
gal argument and its argument schemes represent guments that have succeeded, or failed, in the past.
the above-enumerated key features of legal argu- Consider a simple query discussed in (Ashley and
ment. As illustrated in the callout boxes of Figure Walker, 2013a): Q1: “MMR vaccine can cause in-
3, the model indicates: (1) the 1st Althen rule and tractable seizure disorder and death.”
causation-in-fact concept that govern the decision An attorney/user in a new case where an injury
of the causation issue, (2) the preponderance of ev- followed an MMR vaccination might employ this
idence standard of proof governing the court’s as- query to search for cases where such propositions
sessment, (3) support relations among the proposi- had been addressed. Relevant cases would add
tions, the Special Master having recorded no coun- confidence that the propositions and accompany-
1.
rule
and
2.
standard
5.
aYribu7on
legal
FACTOR
[1
of
4]
:
"MMR
of
proof
info
vaccine
causes
fever."
concepts
AND
[1
of
2]
:
The
injury
of
Dr.
Kinsbourne
and
Dr.
Eric
Fernandez
"was
[or
Kohrman
agree
that
were]
caused
by"
the
MMR
MMR
vaccine
causes
vaccine
received
in
the
fever.
vaccina=on
on
November
5,
1996
(42
USC
300aa-‐11(c)(1)(C)(ii)).
FACTOR
[2
of
Q1
4]
:"[F]ever
causes
AND
[1
of
3]
:
(1)
seizures."
Dr.
"MMR
vaccine
Kinsbourne
and
Dr.
A
“medical
causes
fever"
Kohrman
agree
that
OR
[2
of
2]
:
OFF-‐TABLE
theory
causally
and
"fever
fever
causes
seizures.
INJURY:
The
"causa=on-‐ connect[s]”
the
causes
in-‐fact"
condi=on
is
vaccina7on
on
seizures."
"Ms.
sa=sfied
(Althen,
418
F. 11-‐5-‐96
and
an
Cusa7
has
3d
at
1278,
1281).
intractable
provided
more
seizure
disorder
FACTOR
[3
of
4]
:"[A]
than
and
death
child
who
suffers
a
preponderant
(Althen,
418
F.3d
complex
febrile
seizure
evidence".
at
1278).
has
a
greater
chance
of
developing
epilepsy.”
the
MMR
vaccine
was
"not
only
a
but-‐for
cause"
of
an
intractable
6.
plausibility
3.
support
/
seizure
disorder
and
death,
"but
FACTOR
[4
of
4]
:
"[T]he
also
a
substan=al
factor
in
medical
literature
...
aYack
rela7ons
bringing
about"
an
intractable
do[es]
not
assist
the
(no
aYacks
here)
seizure
disorder
and
death
4.
cita7on
of
special
master
in
(Shyface,
165
F.3d
at
1352-‐53;
authori7es
evalua7ng
Ms.
Cusa7's
Althen,
418
F.3d
at
1278).
'legal
cause'
claim."
Figure 3: Diagram of DLF Model of Special Master’s Finding in Cusati Case re 1st Althen Condition
ing arguments were reasonable and had been suc- client sustained seizures after receiving the MMR
cessful. vaccine probably knows that he/she will have to
Importantly, the cases retrieved will be more satisfy a requirement of causation. The attorney
relevant to the extent that the proposition is used in may not know, however, what legal standard de-
a similar argument. That is, they will be more rel- fines the relevant concept of causation or what
evant to the extent that the proposition plays roles legal authority may be cited as an authoritative
in the case arguments similar to the role in which source of the standard. In that situation, retrieved
the attorney intends to use it in an argument about cases will likely be more relevant to the extent that
the current case. that they fill in the legal rule-oriented direction,
relative to a proposition similar to the one marked
An argument diagram like that of Figure 3 can
“Q1”, with legal rules about the concept of causa-
illustrate the effect of the six key elements of le-
tion and citations to their authoritative sources.
gal reasoning illustrated above on how relevant a
retrieved case is to a user’s query. The diagram If the attorney is unsure of the kinds of evidence
shows a legal argument in which the proposition that an advocate should employ in convincing a
corresponding to Q1 plays a role in the Cusati case Special Master to make the finding of fact on cau-
as an evidence-based finding of the Special Mas- sation or of the relevant standard of proof for as-
ter, namely, that “MMR vaccine causes fever” and sessing that evidence of causation, retrieved cases
“fever causes seizures.” will be more relevant to the extent that they fill in
Such diagrams have a “legal rule-oriented” di- the evidentiary factors-oriented direction, relative
rection (i.e., to the left in Figure 3) and an “eviden- to a proposition similar to the one marked “Q1”,
tiary factors-oriented” direction (i.e., to the right with evidentiary factors and an identification of
in this diagram). For instance, an attorney whose the standard of proof.
The attorney may be interested in better un- Evidence: sentences that describe any type of
derstanding how to improve the plausibility of a evidence legally produced in the particular
proposition about causation as an evidence-based case being litigated, as part of the proof in-
finding. Cases will be more relevant to the extent tended to persuade the trier-of-fact of alleged
that they contain evidentiary factors that support facts material to the case (e.g., oral testimony
such a finding. An attorney interested in attack- of witnesses, including experts on technical
ing the plausibility of the evidence-based finding matters; documents, public records, deposi-
might be especially interested in seeing cases in- tions; objects and photographs)
volving examples of evidentiary factors that attack
such a finding. Citation: sentences that credit and refer to au-
Finally, the cases will be more relevant to thoritative documents and sources (e.g., court
the extent that the proposition similar to the one decisions (cases), statutes, regulations, gov-
marked “Q1” concerning MMR vaccine’s causing ernment documents, treaties, scholarly writ-
injury is attributable to the Special Master as op- ing, evidentiary documents)
posed merely to some expert witness’s statement.
In the “text”, “concept”, and “citation” slots of
6 Specifying/Determining Propositions’ the appropriate nodes of the query input diagram,
Argument Roles Figure 4, users could specify the propositions,
concepts, or citations that they know or assume
The importance of a proposition’s argument role and check the targeted nodes in the directions
in matching retrieved cases to users’ queries raises (rule-oriented or evidentiary-factors-oriented) or
two questions: (1) How does the user specify the ranges that they hope to fill through searching for
target propositions and their argumentative roles cases whose texts satisfy the diagram’s argument-
in which he is interested? (2) How does a pro- related constraints. In effect, the diagram will
gram determine the roles that propositions play in guide the IR system in ranking the retrieved cases
retrieved case arguments? for relevance and in highlighting their relevant
An argument diagram like that of Figure 3 may parts.
play a role in enabling users to specify the argu- Regarding the second question, concerning how
ments and propositions in which they are inter- a program will determine propositions’ argument
ested. One can imagine a user’s inputting a query roles in case texts, that is the third task that
by employing a more abstract version of such a di- Mochales and Moens addressed with a rule-based
agram. For instance, in the Query Input Diagram grammar applied to a small set of documents.
of Figure 4, the nodes are labeled with, or refer to, While their rules employed some features partic-
argument roles. These roles include: ular to legal argument, (e.g., whether a sentence
referred to a legal article) one imagines that ad-
Legal Rule: sentences that state a legal rule in the ditional features would be needed, pertaining to
abstract, without applying the rule to the par- legal argument or to the regulated domain of in-
ticular case being litigated terest. These features would become the predi-
cates of additional grammar rules or be annotated
Ruling/Holding: sentences that apply a legal rule
in training cases for purposes of machine learning.
to decide issues presented in the particular
The legal argument roles listed above are a first
case being litigated
cut at a more comprehensive enumeration of the
Evidence-Based Finding: sentences that report types of legal argument features with which to an-
a trier-of-fact’s ultimate findings regarding notate legal case texts in an Unstructured Infor-
facts material to the particular case being lit- mation Management Architecture (UIMA) anno-
igated tation pipeline for purposes of extracting argument
information and improving legal IR.
Evidence-Based Reasoning: sentences that re- UIMA, an open-source Apache framework, has
port the trier-of-fact’s reasoning in assessing been deployed in several large-scale government-
the relevant evidence and reaching findings sponsored and commercial text processing appli-
regarding facts material to the particular case cations, most notably, IBM’s Watson question an-
being litigated (e.g., evidentiary factors) swering system (Epstein et al., 2012). A UIMA
✔
Evidence-‐
✔
✔
✔
Based
Finding
Evidence-‐
Based
Primary
Legal
Secondary
Ruling/Holding
Evidence
Rules
Legal
Rules
text:
“MMR
vaccine
Reasoning
can
cause
in-‐
concepts:
causa/on
concepts:
causa/on
tractable
seizure
concepts:
causa/on
cita,ons:
cita,ons:
disorder
and
concepts:
cita,ons:
concepts:
death.”
concepts:
causa/on
Figure 4: Sample Query Input Diagram
pipeline is an assemblage of integrated text anno- source credibility to resolve evidentiary dis-
tators. The annotators are “a scalable set of coop- crepancies (e.g., in terms of expert vs. expert
erating software programs, . . . , which assign se- or of adequacy of explanation) (Walker et al.,
mantics to some region of text” (Ferrucci, 2012), 2014) .
and “analyze text and produce annotations or as-
sertions about the text” (Ferrucci et al., 2010, p. If we succeed in designing a system of coordi-
74). nated legal annotation types and operationalizing
A coordinated type system serves as the basis a UIMA annotation pipeline, we envision adding
of communication among these annotators; a type a module to a full-text legal IR system. At re-
system embodies a formalization of the annota- trieval time it would extract semantic / pragmatic
tors’ analysis input and output data (Epstein et al., legal information from the top n cases returned by
2012, p. 3). In (Ashley and Walker, 2013b) and a traditional IR search and re-rank returned cases
(Ashley and Walker, 2013a) the authors elaborate to reflect the user’s diagrammatically specified ar-
three additional bases for annotations, which, with gument need. The module would also summa-
further refinement, may serve as a conceptual sub- rize highly ranked cases and highlight argument-
strate for the annotation types listed above: related information (Ashley and Walker, 2013a).
Since the module processes the texts of cases re-
1. DLF annotations, as suggested in Figure 3,
turned by the information retrieval system, no spe-
capture “(i) the applicable statutory and reg-
cial knowledge representation of the cases in the
ulatory requirements as a tree of authoritative
IR system database is required; the knowledge
rule conditions (i.e., a “rule tree”) and (ii) the
representation bottleneck will have been circum-
chains of reasoning in the legal decision that
vented.
connect evidentiary assertions to the special
master’s findings of fact on those rule condi- 7 Conclusion
tions (Walker et al., 2011).”
According to Wittgenstein, meaning lies in the
2. Annotations in terms of presuppositional in-
way knowledge is used. Legal argument models
formation that “identifies entities (e.g., types
and argument schemes can specify roles for legal
of vaccines or injuries), events (e.g., date of
propositions to play (and, interestingly, Stephen
vaccination or onset of symptoms) and re-
Toulmin was a student of Wittgenstein.) Thus, re-
lations among them used in vaccine deci-
searchers can enable machines to search for and
sions to state testimony about causation, as-
use legal knowledge intelligently in order, among
sessments of probative value, and findings of
other things, to improve legal information re-
fact.” (Ashley and Walker, 2013a).
trieval.
3. Annotations of of argument patterns based Although IBM Debater may identify argu-
on: inference type (e.g., deductive or statisti- ment propositions (e.g., claims), legal argument
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